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1 - Determining the factors affecting the collective financing of knowledge-based IT companies
Ali Haji Gholam Saryazdi ali rajabzadeh ghotri alinaghi mashayekhi alireza hassanzade
20.1001.1.27170411.1397.10.37.4.4

The method of crowdfunding in the world has expanded rapidly due to the need for financing in the early stages of start-up businesses as well as advances in information technology. In Iran, several financing platforms have been established so far, some of which have bee More

The method of crowdfunding in the world has expanded rapidly due to the need for financing in the early stages of start-up businesses as well as advances in information technology. In Iran, several financing platforms have been established so far, some of which have been successful and some of which have been unsuccessful. Therefore, it is necessary to help the development of this method by examining the factors affecting it. Since mass financing is a new and new phenomenon, it is necessary to increase its awareness in the society in an appropriate way while determining the factors affecting this method. The collective modeling method is based on social networks and the Web 2 with the aim of recognizing new phenomena. Therefore, in this article, using collective modeling, the factors affecting crowdfunding in Iran in order to support start-up companies in the field of IT are discussed. Manuscript profile
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2 - User recommendation in Telegram messenger by graph analysis and mathematical modeling of users' behavior
Davod Karimpour Mohammad Ali Zare Chahooki Ali Hashemi
20.1001.1.27170411.1400.13.49.10.5

Recommender systems on social networks and websites have been developed to reduce the production and processing of queries. The purpose of these systems is to recommend users various items such as books, music, and friends. Users' recommendation on social networks and i More

Recommender systems on social networks and websites have been developed to reduce the production and processing of queries. The purpose of these systems is to recommend users various items such as books, music, and friends. Users' recommendation on social networks and instant messengers is useful for users to find friends and for marketers to find new customers. On social networks such as Facebook, finding target users for marketing is an integrated feature, but in instant messengers such as Telegram and WhatsApp, it is not possible to find the target community. In this paper, by using graph and modeling the intergroup behavior of users and also defining features related to groups, a method for recommending Telegram users has been presented. The proposed method consists of 8 steps and each step can be considered a separate method for user recommendation. The data used in this paper is a real data set including more than 900,000 supergroups and 120 million Telegram users crawled by the Idekav system. Evaluation of the proposed method on high-quality groups showed an average reduction in error by 0.0812 in RMSE and 0.128 in MAE. Manuscript profile
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3 - A Recommender System Based on the Analysis of Personality Traits in Telegram Social Network
Mohammad Javad shayegan mohadeseh valizadeh

<p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of perso More

<p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of personality traits of individuals has always been one of the interesting research topics. In addition, achieving personality traits based on data obtained from individuals' behavior is a challenging issue. Most people spend most of their time on social media and may engage in behaviors that represent a character in cyberspace. There are many social networks today, one of which is the Telegram social network. Telegram also has a large audience in Iran and people use it to communicate, interact with others, educate, introduce products and so on. This research seeks to find out how a recommendation system can be built based on the personality traits of individuals. For this purpose, the personality of the users of a telegram group is identified using three algorithms, Cosine Similarity, MLP and Bayes, and finally, with the help of a recommending system, telegram channels tailored to each individual's personality are suggested to him. The research results show that this recommending system has attracted 65.42% of users' satisfaction.</span></p> Manuscript profile

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